{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "from preamble import *\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hide_input": false
   },
   "source": [
    "## Introduction\n",
    "### Why Machine Learning?\n",
    "#### Problems Machine Learning Can Solve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Knowing Your Task and Knowing Your Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Why Python?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### scikit-learn\n",
    "#### Installing scikit-learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Essential Libraries and Tools"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Jupyter Notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### NumPy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "uuid": "e2b8e959-75f0-4fa9-a878-5ab024f89223"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x:\n",
      "[[1 2 3]\n",
      " [4 5 6]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "x = np.array([[1, 2, 3], [4, 5, 6]])\n",
    "print(\"x:\\n{}\".format(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SciPy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NumPy array:\n",
      " [[1. 0. 0. 0.]\n",
      " [0. 1. 0. 0.]\n",
      " [0. 0. 1. 0.]\n",
      " [0. 0. 0. 1.]]\n"
     ]
    }
   ],
   "source": [
    "from scipy import sparse\n",
    "\n",
    "# Create a 2D NumPy array with a diagonal of ones, and zeros everywhere else\n",
    "eye = np.eye(4)\n",
    "print(\"NumPy array:\\n\", eye)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "SciPy sparse CSR matrix:\n",
      "   (0, 0)\t1.0\n",
      "  (1, 1)\t1.0\n",
      "  (2, 2)\t1.0\n",
      "  (3, 3)\t1.0\n"
     ]
    }
   ],
   "source": [
    "# Convert the NumPy array to a SciPy sparse matrix in CSR format\n",
    "# Only the nonzero entries are stored\n",
    "sparse_matrix = sparse.csr_matrix(eye)\n",
    "print(\"\\nSciPy sparse CSR matrix:\\n\", sparse_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "COO representation:   (0, 0)\t1.0\n",
      "  (1, 1)\t1.0\n",
      "  (2, 2)\t1.0\n",
      "  (3, 3)\t1.0\n"
     ]
    }
   ],
   "source": [
    "data = np.ones(4)\n",
    "row_indices = np.arange(4)\n",
    "col_indices = np.arange(4)\n",
    "eye_coo = sparse.coo_matrix((data, (row_indices, col_indices)))\n",
    "print(\"COO representation:\\n\", eye_coo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "uuid": "30faf136-0ef7-4762-bd82-3795eea323d0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f1d73e83ba8>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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oICcnjd69U4L2d51OB1dcoZz2X/1qs20Xp1Ayf/5IysoaAJg6VX0ug1Xee/31g0hJiaOysvGU52pYlWAIQz4wTAiRI4RIQi3+J1UXCSFGAJnAKr/HMoUQyd7fewJnAZuDMCZDO6ioaGD9+mPExwumTAneou10OliyZBpCwNdfV5q+Sa2wapUSZe0tBJNrrlHtGT755LDpm9QKq1er+Z82Lbjzv3Jlia/S7Pnnd9uyCCBgYZBSNgC3Au8DW4DFUspNQogHhRD+VUZzgIVSSv8w0yigQAjxJeACHpFSGmEIE/n5pTQ2SiZM6EZ6eucat7XGzJm9GTIkDYBzzsk2C1MLhFIY6upUtDYlJc70TWqB6upG1q8/hhD42sUHA+0dX3WVCqJcdFEfW3YACMo+BinlO1LK4VLKoVLKBd7HHpBSLvW75ldSyrtPeN1nUspxUsoJ3p/PBWM8hvbx6aeqTDWYYSSNy+X2HRP67ruHbPfFCAc6jBdsYXC53Nx66zoyMxOpqfHw+OMTbbk4hZJ1647S0CAZO7YbGRkd74/UGroDgBYGt7vWlq1JzM7nGES3A/BPPAczOaytpnvuUfHakSMzzMJ0AiUltezYUUFqaryvMixY6MVJC35CQpwtF6dQsnq1motp04Kb99IdALQXUlBQytlnZ9uuA4ARhhhEtwP473+bqruCmRzWC9MNN6hukzt3VrJo0TSzMPmh49u5uZkkJgb3a6gXp8mTmxYn0zepOaHKL2j69EmlX79Uysoa2LatPCTvEUqMMMQgTqeDvLzxVFY2kpGRwG23rQtqclgvTH37ptC7dwrHj9czcGCaWZho3lEVVBgpVKW8Wujz848G/W/bnVALA+Ar6LDj/BthiHECaZx3KoQQTJqkrNbPP7fflyMUaG9NH8yTnp4QslJe/7n3eFrbWhQ7aFHev7+affuq6do1gYMHq0O2v0aHk+zoKRthiFGWLVNbTZzO7JBWrfiHMwzKW3vllWl8+eVxAB5/fHvISnl7906hf/9UKirsGc4INlqUn31W9ac67bR0rr56dcj21+i/u3at/T77RhhiEP8T2+bPHxm0Hj0tYTyGk+nXr+no1FtuCe0eAxNOakK3HHn00a0AFBaWh3R/jTaK1q8/Rl2dJyTvESqMMMQga9eWkpCgOpmcfnr3oPXoaQkTzjiZl17aA8Dw4ekh32NgPLbmOJ0OHI5kAC69tE9IRbl79ySGD0+nttbDxo3HQ/Y+ocAIQwzy3e/2p6bGQ58+KfTqpVoxhKpqpW/fVPr0SaGsrIGvv7ZvG+Jg4XK5+cMftgMwZ07wOqq2RpMwGI8BVEfVvXv1ueTukIpyXl4hAwZ0AZrCSXbpGWaEIQZZt04tEhMndg/L+5nFqYn8/FLGjOkKqPkPpbcGMHmyCiWtW3eMhgZ7hTOCjcvlZvbs1UipjvEMVkfV1sjNzfJVP+Xnl9qqZ5gRhhhEtwU+/fTwCIPJMzRx110j2LNHWaxamEO5x+DZZ3fSp08K1dWNbN5cBtjHag02+fml/PznwwA198HqqNoaTqeDRx4ZD8Bbbx20VTNDIwwxyPr1WhiC1yOmLbTVajwGOHSohpKSWrp1S2TQoC4hf7/c3CxKS+sANf92slqDzfz5I/F4naYJE0IvygA33piDEKo1xk03DbGFKIARhpgknB5DXl4hNTWqodsXX6gEdKxarNAkyhMndkeIlo4yCS5Op4Pvf38wAE8+ucNWVmso0IcYhSuMunr1Ed8BWH/7m32aGcaEMOiNLf7E6uJ06FANhw7VkJGRQE5OWsjfLzc3ix//+HN69kyivLyBF1/cE7MWKzQXhnAxe3Z/AD7//FjMt+DW8z9hQnD7U7WE9s70aYY33zzENj3DYkIY9MYW/R8Sy+60/8KkLZlQopOr+lCUYLffsBuREAY990lJIqZbcJeX1/P115UkJgpGjuwa8vfTPcPOO68XAPX1Hts0MwxuE36L4nQ6+Oc/pzBr1sc4HMlUVzfG7OKkK5LClXgGNf9Tp2bx8ceHGT++W0zOuybcwuByubn55gIyMxM5erSeP/7x9JgNJ23YoPYSjBnTjaSk0NvEOndRWamE+auvjvOHP0y0xbzHhMcAcOGFvQHYt6+auXMH2eI/JxQ05RfCk3gGtTjpBTE//2jMWqwVFQ1s315BYqJg9OjQW6zQZLXqyrD09ATbWK3BJpxhJH/Gj1dGwFdf2WeTW1CEQQhxoRBiqxBihxDi7hae/74QokQIsd57u8nvuXlCiO3e27xgjKclouG4vUDQeRZ/izUceRYdtvvznycCkJmZaJs4a7DZsOE4UsLo0V3DYrFCU6dbvTht2HA8Zltw6/5UuiIpXAwYkEq3bomUlNRSXFwT1vfuLAF/OoUQ8cATwCxgNDBHCDG6hUsXSSknem/Pel+bBfwSmApMAX4phAi6KasXp1mzlNdwzTUDY25xys3NYvbsVT6L1e2uCUueRVus1147iOTkOIqLa3n++dyYtljDmV/QjBunrOSvvjoW9ve2CpGafyGE7zAmu3gNwTBbpgA7pJQ7pZR1wELg8na+9gLgAyllqZTyKPABcGEQxtQMvThdcIEShpqaxphzp51OBw88oPS6R48krr12TVjizNpiTUiIY9QoFT7JykqKKYs1Ut6aP1oYdJw91mhslL5/e7g9BsBPGOwhzMEQhn7APr/7Rd7HTuS7QoivhBCvCiEGdPC1AaEXJ/3l2LgxNt3p+HhVhXToUG1EyhZjdXE68cQ8KWXYq+JGj+5KXBxs21bh21cSS+zYUUF1dSP9+6eSlZUU9ve3W54hGMLQUs3jiW00lwGDpZTjgQ+BFzrwWnWhEDcLIQqEEAUlJSUtXXJKxo5VC9OmTWUx2enz/fcPAXDeeb0iUrbYJMxlYX3fSKPOYJhKYaE6E2HBgsKwVwWlpsYzbFgGjY2SLVtiZ/6t4K2BfygvdoShCBjgd78/cMD/AinlESllrffuM8Ck9r7W7288LaWcLKWcnJ2d3amBZmUl0bdvClVVjezaVdmpv2FXXC43776rhOHOO4eHvKtnS8SqxwAwYEDTZsJQn8HQGrE4/9pbW7p0PwDduiVGZA/T2LEqjLp5cxn19dZvZhgMYcgHhgkhcoQQScDVwFL/C4QQffzuXgZs8f7+PnC+ECLTm3Q+3/tYyNBeg936owfK2rWlpKbGA6qOO9RdPVvCf2GSMrY8toUL9wIwdGhaxDaZ6Th3LAmD/pwvWVIEwNKlByKyhyMjI5EhQ9Koq/PY4jS9gIVBStkA3Ipa0LcAi6WUm4QQDwohLvNe9j9CiE1CiC+B/wG+731tKfAblLjkAw96HwsZWhhi6csBcP31gygvb6Bbt0T69g3tGQyt0bdvCt27J1JaWsfBg/Yo2wsGKnShTg379rf7RcRbA/uFM4KF0+nwGUXXXRe5PUx2qkwKSjG1lPIdKeVwKeVQKeUC72MPSCmXen+/R0o5Rko5QUrplFIW+r3271LK07y354MxnraI1Tj3pk3q3zt2bNewNG9rCSFETIYz8vNLmTJFhS4i5a1BbIaSAN566wBlZQ3ExcHixfsi4q3l5RWSkZEINAmDlfu1xczOZ42O9cVaKEkLw5gx4d31eSKxuDjNnz+Sw4dVik0f0hOJqricnDTS0uI5eLDGN55ox+Vyc911awEVLYiUt5abm8Wbb6o8x4YNxy3fry3mhGH06K4IAVu3ltvugO5A0EKoF6ZI4V8yHCvU13vYulXFlfVejkjwu99t9R01qYXZylZrMMjPL+WHP8wB1Hc/Ut6a0+ngL385HYCPPy6xfL+qmBOGLl0SGDo0nYYG6fuyxgJNHoM1hCGWPIYdOyqor5cMHtyF9PTI9a3Mzc1i925VjWcHqzUY+B/OE0lvDeCaawaRkCAoK2vghhsGW1YUIAaFAWIvnCSlZNMm9W/VyfdIkJdX6DtNbPPmMhobY+PQHquE8ZxOBzfdpKzn55/fbXmrNVjoI03D1biwNfQGR4Bnntll6ZY8MSkMsWa17t9fTVlZAz16JOFwJEdsHLm5WfzgB/lkZydRU+PhX/+KjUN7tChHemEC+O531aE969fHzqE9VhBm7Z2dfbbag3XTTTmW7tcWc8Lgb51qjyHarVZdgTVmTOQqkqCppvz48Xogdg7tsUoYD/B5bLFyaE95eT1791aRlBTH0KGhP7GwNXS/thkz1Gfd48HS/dpiThhyc7P4y192AGrBjIU4qxXCSBqn08GkSWquTz+9e9SLAjSFMiItDC6Xmx/96HMyMhKoq5M8+eQZlrZag8GWLSqPOGJEBgkJkVvudL827TVu2VJm6X5tMScMTqeDRYumAbBrVyWzZ0d/nNVKFqvL5fZ5amvWlEb1ogSqIknvdI1kRRI0Wa16o1VmZpKlrdZgYBVR1mhh0OOyKjEnDADnndfbF2u/4oq+US0K4F+qGlmPQXtnjzwyDoB+/VKj3mLdvl1VJKk9BJE9SdduVmswsFJ+B2DIkDSSkuLYs6eKioqGSA+nVWJSGFwuty/OvXhxUVQvTB6PtIzVpC3WuXMHASop/sor06LaYtULU6Tn3h/tuVjdag0GVvnsaxIS4hg+PB2AwkLrzn/MCYO2Wr/3PdXU9ZJL+kSt1ZqXV8iiRfuorGykV69kevZMjmiiXVusXbsm0q9fKrW1HnJy0qLcYrXWwgT4eQzRv49Hz79VPAawRzgp5oRBW60XXaQavlZVRe9pbrm5Wfz4x58DKoxkpUT7qFEZgLW/HMHACqWSJxIrHkNFRQN79qiKpNNOS4/0cHzYQZhjThi01aoXpmiOszqdDp9nVF5eb6kNTf5x7mhEHxDjH0qySln0gAGppKcnUFJSG9U9k/RnK9IVSSdiB2G2zmyFmeHDMxACvv66Mqp7JtXXq3MP8vOPWmpDk/5yWNlqCgR9QMzWreUIAcXFNZbx1oQQjBzZZBhFK1bZ8XwiJpRkYVJS4snJSaOxUbJ9e3QuTgCrVx8B4LrrBlpqQ1OTMFj3yxEITqeDvLzxeDzq1LDrr19rGW8N7BHO6CxN3lpTfscq3hrAsGHpxMcLdu5U51BbkZgVBoh+q3XFimJfo8Df/nZCxFoOt4R/KC9aT3PT/fePHau3lLcG9ghndBbtreneRB6PtIy3BpCcHM9pp6Xj8WDZ09yCIgxCiAuFEFuFEDuEEHe38PzPhRCbhRBfCSGWCyEG+T3XKIRY770tPfG1ocR/cYpGVqxwIyV0756Iw5EcsZbDLZGdnUyPHkmUlTVw4EB0nub2zjsHAZg2LctS3hpEd47nxM/5H/+43VLeGlh/7QlYGIQQ8cATwCxgNDBHCDH6hMvWAZOllOOBV4E8v+eqpZQTvbfLCCPRHs4499xegPp36h5JVkm0CyGiev5dLjcvv6zOeb7ppiGW8tYg+qvCpk3r4Wu3bTVvDayfZwiGxzAF2CGl3CmlrAMWApf7XyCldEkpq7x3VwP9g/C+ARPtoaTCQvXv0olGq2F1qykQ8vNLGTRIHYozcmSGpbw1UKe5JSfHUVRUTVlZfaSHE3ReemkPAD16JPH00zstI8jQvJGnFgYr5UAgOMLQD9jnd7/I+1hr3Ai863c/RQhRIIRYLYS4orUXCSFu9l5XUFJS0tplHUIvTFu3luPxRF+cWy+4ke7R0xrRLMx33jmC/furgSZhtoq3BnoHrhqXNiCiBZfLzc9//iUA3/xmT8t5a7m5WTz55NeAEgYr7S/SBEMYWurj3OIqK4SYC0wGfuv38EAp5WTgGuBxIcTQll4rpXxaSjlZSjk5Ozs70DED0L17Er17p1Bd3ciePVWnfoHNsL7HEL2hpP37q6msbPTmUiJ3BkZr5OUV0qNHEtA0/1azWjtLfn4p3/mOsk1HjozccZ6t4XQ6ePnlqYD6jlppf5EmGMJQBAzwu98fOHDiRUKImcAvgMuklL5dNVLKA96fO4GVwOlBGFO7ieZwhtU9BqvHWQPB6qKcm5vF2rVqobSq1dpZ5s8fSW2tSjBY0VsDmDWrD927JyIlzJ7d31KiAMERhnxgmBAiRwiRBFwNNKsuEkKcDjyFEgW33+OZQohk7+89gbOAzUEYU7uJVqu1vLyeoqJqkpLiGDy4S6SHcxJ5eYXvoWU2AAAgAElEQVTs2FFOWlo8bnctpaV1UWOxgvVF2el0cOedwwF4/fX9lrRaA8HqwuxyuamsVN1VX3ppr2XCXJqAhUFK2QDcCrwPbAEWSyk3CSEeFELoKqPfAunAkhPKUkcBBUKILwEX8IiUMiLCEG1xVr1/YdiwdEu1A9Dk5mbxve+tpl+/VEAlC6PFYgXrL0wAs2crR3/79gpLVu50Fo9H+j7/I0daT5i1d3bZZX0BuOqqAZbKgUCQ9jFIKd+RUg6XUg6VUi7wPvaAlHKp9/eZUspeJ5alSik/k1KOk1JO8P58LhjjaS95eYXU1qqdh9EWZ9ULk5Ut1sWLp/tyO/feuzHKLFb1ebKyMOjkOGC5fRaBsHdvFdXVjfTpk0K3bomRHs5J6Eae552nysnr6z2WyoFAjO98zs3N4v/+T4nAli3lrFhRHDVWqxY6Ky9MTqeDM8/sAai2BdEiCuDvMVhTmF0uN3PnrqFv3xQA8vLGW85q7SxW99Z0I0/92SgsLLdcDiSmhcHpdLBkyTSEUIekX3XV6qixWq3uMYBanD7//CgA69Ydi4pFCeD48XoOHqwhJSXOt5fBamir9fTTMwHVz8lqVmtnafLWrPvZhybh2rq13HJtYWJaGABmzOjls5ouvrhPVIgCWN9q0nHWxx+fCEDPnklRZLE2tXuOi2upmjvyaKt1xIimvQxWs1o7i9U/+xqHI5nu3RM5dqwet9ta7c9jXhhcLjeHD9cB8O9/74+KhamhwePrGKu/+FZDW6zXXDOQuDg4dKiGf/1rapRYrNYOI/mjF89oKr6wizAIIZoJs5WIaWHQVuvcuQMBuPDCXra2WnW74Z07K6mvlwwc2IW1a0stmUzXFmtycjxDhqhOk/37p0aVxar3yFgZ/3BGtGCXUBL4C7O1yuVjWhii7ZhP3W548WLVocThSLZFMj3aFqemxL/1F6YmizU62p8fPVpHcXEtXbrE079/aqSHc0r8E9BWIiHSA4gk2jrVO291nNWueQZdAnrppZ8A6rzht9/+huX/PSNGZPDWWwct9+XoKHl5heTmZjULZbhcbvLzSy3rCWVnJ5OZmcjRo/UcOlRDnz7WX0zbQs+9lfM7/ljVKIppj0EzdGga8fGC3bsrqamx5olK7cXpdPgOPnc6sy0vChA9cW7tsW3fro7zPHjQOsd5toY65lNZrVZbnDqDDslYuRrPH6t+9o0woE5UyslJw+OBHTsqIj2cgPA/0vDTT4/YIl+iwxl2X5icTge/+13TcZ7XXbfGFuXPVl2cOoLOr53orVkxv+bP0KHpJCQoo9RKx3waYfBiVZeuI+hkekqK+m999tlJtkimN8VZ7R/n7tZNdSy14nGerWHVypiOoL21Tz45DKjKPKt7awCJiXEMGZKGlFjq7HkjDF6i4cuRn1/KU09NoqKika5dE/jud/vbIpnes2cSmZmJlJU1cOiQvY/5fPttdZzn1KnWO86zNaLBKNL5tTVrjgDw5z/vsIW3BtZMQBth8BINX47580eSna16/48YkYEQwhablqIlzu1yuXnxRXVy2A03DLbcATGt4e+x2ZmzzurpO87zxz+2h7cG1lx7jDB48S/bszNW7irZFtEQ587PL2XIkDRAfZ6sdkBMawwZkkZCgmDPniqqqhoiPZxO8/LLe5ASundP5JlnrHWcZ1tY8bNvhMGLv8Vq5zi3f7menYiGBPRdd43whcL0v8cOHltiYhxDh6Z749z2LL5wudz8v/+3HoDp03vYxlvLyyukslIlnbVRaoWkuREGL9ES527yGOwlDFa0mjpKSUktR4/W07VrAr17p0R6OB3CiuGMjpCfX8pVV6nzJezkreXmZvGrX20C1Gd/+XJrdHgOijAIIS4UQmwVQuwQQtzdwvPJQohF3ufXCCEG+z13j/fxrUKIC4Ixns7gH+e28+Jkd4/BzqE8vajq/I5dyMsrJDU1Hmj6/FjBau0I8+ePpKFBefpWPc6zJVSH5+kIoTovWKXDc8DCIISIB54AZgGjgTlCiNEnXHYjcFRKeRrwGPCo97WjUUeBjgEuBJ70/r2IYHerqba2kV27KomLw7fJzS7oeu49e6osVc/dEezUPM+f3Nws3npLVVMVFtr3/Gf/rrZ2wul0+Np3zJrVO+KiAMHxGKYAO6SUO6WUdcBC4PITrrkceMH7+6vAuUKZVJcDC6WUtVLKXcAO79+LCHYvWf3660oaGyWDB6eRkhIxfe0Ujz22jd69k5vVc9vNavX3GOyE0+ng4YfHAU17YaxgtXYEKa19nGdbuFxuSkpU2+033zxgibxIMIShH7DP736R97EWr/GeEX0c6NHO14YNu3sMds0vgLJa3W7V/nzr1nJbWq12nv9rr1Udhg8dqrVVqafGP7/Tq1dypIfTbvTn/PvfHwzAzJkOSyTNgyEMLQVTTyzrae2a9rxW/QEhbhZCFAghCkpKSjo4xFOTl1fIsWNqYbJSdUBHsKsrDcpq/fa3lU3wxBNf29JqtWt+B2D9+mPotMiTT9pjY54/ds3v6A7PF1+sOjxXVDRYImkeDGEoAgb43e8PHGjtGiFEAtANKG3nawGQUj4tpZwspZycnZ0dhGE3Jzc3izvv/Iq4ONizp4r33jtkY4vVXq605vzz1eHoH31UYpt2Eho753e01Tp2rPrc3HPPSEtYrR3Brvkdq57/HAxhyAeGCSFyhBBJqGTy0hOuWQrM8/5+JbBCqs0CS4GrvVVLOcAwYG0QxtRhdHmbECAlXHONNaoDOoKdLVaAykq1uapPnxTbtJPQ2Dm/o63WqVN7AJCSEm8Jq7Uj2DW/oxk8uAtJSXEUFVVTURH5TYYBC4M3Z3Ar8D6wBVgspdwkhHhQCHGZ97LngB5CiB3Az4G7va/dBCwGNgPvAT+VUkasJMXpdDB0qLL2Zsyw17kMzZNv9vtyuFxufv3rzQCUldWzaNE0W1mtdp77Jqu1qWTYClZrR7Dz/AMkJMT5PM1t2yKf4wzKPgYp5TtSyuFSyqFSygXexx6QUi71/l4jpZwtpTxNSjlFSrnT77ULvK8bIaV8Nxjj6Swul5t9+6oBePfdQ7ZZlADc7lqOHaunW7dEHA77JN80+fmlLFkynezsZCorGxk+PMNWVqud8zsaO/ersru3DNYqfjE7n73oOOuttw4F1LZ6O1isug+9vyu9cmWJrZLm0GS1+pcM28lqtXsoA+xbrm3n/I4/Vpp/IwxedJz1iitUZczRo3W2sFh1H/o331Q5++7dE22XNPfHSlZTR7Br8tMfq8W524ud8zv+WOmzH9NnPvujLdMjR9RGk61byznnHOsfjamT5hdfrM55/uSTw7z1lvXPeW4Nu/VMyssrZPLkzGYeg9XPeW6NhIQ4hg1LZ9OmMrZtK+eMMzIjPaR2Yff8gsZKbWGMx3ACPXok++Lc+/dXR3o47cLpdNCvn2radtFF1thS31nsdjZAbm4Ws2ev9uV3Nm8+bmuPzUrhjPYSDfkdaBr/tm0VeDyR7fBshKEF7Ga1ulxudu6sBGD5crfl8yJtYbf2206ng/vvHwVAWlo83/ue/cqc/bFSOONUnJhfGzmyq+02pfrTvXsSvXolU13dyL59VREdixGGFrCTMOikuccD8fGChQvtVeZ5IjrOvW+ffeLcXbqoiOyBAzW225h3IlYKZ5wKnV9bs0blAauqGmztrYF1jvk0wtACdvpy5OeX8sgj4wEYOjSN88/vbYukeWvoODdYo567PXzwwSEAnM5s223MOxGrLEztwel0sGjRNJ/H8JvfbLG1twbW8diMMLSAnb4c8+ePpGtXZbHqcdupzLMl7Oax6Yqw//mfYbY5Oaw1muLc5RGPc7eHMWO6oQ9cvOUWe3trYJ0cjxGGFrDTwgT+pZL2Tr5p7JRnyM8vJSsrCVDzb5eTw1qjW7dEevdOoabGw969kY1zt4eXX94DQL9+Kfztb/b21sB4DJZm8OA0kpLi2L+/mvLy+kgP55REmzDYqTLp1ltPw+2uJSFB+NqpRIvHFunF6VS4XG4eeEC1UTnvvN6299by8gopLbVGh2cjDC0QHy8YPlzHua1/OPqWLepDZOfNVf7YyWPbvr0CKdUJdImJ0fF1sko441Tk55f6OvKOGmV/by03N4vbb19PYqLgwIEa3n77QMSS6dHxSQ4BdklAezz2bp7XEnaKc2tRHjUqOuY+L6+Q+Hh1nkGkrdZTMX/+SF/lWjTk17SweTzq/ty5ayOWTDfC0Ap2SUAXFVVTVdVIr17JZGYmRXo4QaFr10T69LFHnDsaWmH4k5ubxb/+peL2djhJT89/tAiz0+nwRSvOPjtynReMMLSCXcIZ2qqLloUJlNWqj2fU829VqzXa8jtOp4O//nUSAGvXllr6JL2Kigb27q0iMVGQk5MW6eEEBZfLzZ49yhj6z3+KI5YvMcLQCnZJwEXbwgTKatX/rsLCMktbrVqYR42KHmG++uoBxMdDZWUjN9yQY0lRgKZ9LsOGZZCQYP+lTH/Of/azYQCccUb3iCXT7T+bISAvr5Di4hpAffgaG6WxWMOI0+ngpptyAHjxxT2WtVobG2VUtNs+kY8+KkEfx/7MMzstW+UTbfkd3eH5yivVaceHD0euw3NAwiCEyBJCfCCE2O79eVI7RiHERCHEKiHEJiHEV0KI7/k99w8hxC4hxHrvbWIg4wkWublZfP/7+fTokUhtrYeFC/da3mKNplASwKWX9gXgiy+OWbbNxN69VdTUeOjTJ4Vu3RIjPZygoK3Ws8/uCcAPf5hj2RLQaMvv+J9JIgTs2FHBWWf1jEgyPVCP4W5guZRyGLDce/9EqoDrpZRjgAuBx4UQ3f2ev0tKOdF7Wx/geIKCrg4oK1MVD7fc8oUlLVaALVuiz2MAdR4GQJcu8ZZtM9EkytEz99pqPecc9VmXEsuWgEZb4lmTmhrP4MFpNDZKduyITLl8oMJwOfCC9/cXgCtOvEBKuU1Kud37+wHADWQH+L4hx+l0MGGC0q8pU7IsKQrHjtVx6FANqanxDBzYJdLDCRoul5tbb11HSkocVVWNPPPMZEtarVqUoym/oK1W/W/assW6J+lF2/4df7TY6X9juAlUGHpJKQ8CeH+2uXoKIaYAScDXfg8v8IaYHhNCWOawYpfL7bNIPvnksOUWJWh+nGRcnIjwaIKHtlrHjesGQI8eSZa0WqPRY9BEemE6FQ0NHrZvV9Z0NOV3NE3CbFFhEEJ8KITY2MLt8o68kRCiD/AicIOU0ruFg3uAkUAukAX8bxuvv1kIUSCEKCgpKenIW3cYHWf99a9HA5CTk2Ypi1X3ofdPPFs1Od4ZtNU6erS1rdZoi3H7M2xYBvHxgl27Kqmuboz0cE5i165K6uo8DBiQSnp69B1E6e+xRYJTCoOUcqaUcmwLtzeBYu+Crxf+FldOIURX4G3gPinlar+/fVAqaoHngSltjONpKeVkKeXk7OzQRqK0xXrttYMA1Wd/0aJplrFYdR/6999X7Z6Tk+MsmxwPBP3l2LzZmlZrNFaEaZKS4jjttHSktGb782gWZYi8xxZoKGkpMM/7+zzgzRMvEEIkAa8D/5RSLjnhOS0qApWf2BjgeIKCtlgdjmSyspI4fryekSO7WsZi1cnx117bD8C//73fssnxQGjyGKwlDHl5hbzxxn5KSmpJS4unf//UqPLYNJFenNoi2kpVT0QbRYWFkWkLE6gwPAKcJ4TYDpznvY8QYrIQ4lnvNVcB3wK+30JZ6ktCiA3ABqAn8FCA4wkqQgjf4mQ1q9XpdJCeHg/ANdcMjDpRAOt6DLm5WdxwQz6gLNaVK0uMxxZmot1jyMxsOuYzEm1hAhIGKeURKeW5Usph3p+l3scLpJQ3eX//l5Qy0a8k1VeWKqWcIaUc5w1NzZVSWq6VqVWF4f33D1FaWo8Q8NprRZbJfwSTnJw0kpPjKCqqpqzMOu3PnU4HN96oNuDV1jZadgNeoEQ6zt0SJ+bXRo2KrvyaP5FMQJudz6dAu6pWEgaXy82cOSpVM3x4hu370LdGfLywbAvoxkbl3m/cWGbZDXiBYsVQks6vffXVMQCOHKmLSm8NIivMRhhOgRXj3Pn5pfzkJ0MBNT6796Fvi0iX7bXGxx8fBuCqq/pbdgNeoOgwzbZt5TQ0eE5xdXhwOh387W+TqKxsJDk5jh//+POo9NYgssJshOEUWDGUNH/+SFS+HsaMsX8f+raw4vy7XG7WrTsKwIIF46LWY0tPT2DgwC7U10t27qyM9HB8dO+u2o/U1nqi1lsDE0qyNP36pZKRkcDhw3WUlNRGejg+Nm06DjQtnNGKFcMZ//1vCR4PpKTEkZOTFuUem/Xm/403VDXeGWd0j1pvzf+Yzy1bypEyvI08jTCcAqtWJumxaI8hWrHi3J93nj5OsqvvtLNo9disVpnkcrl59tldAMybNzhqvbXc3Cx++tMvSE2No7S0jtdf3x/WXIoRhnZgtS9HTU0jO3ZUEBenks/RjBV34G7aFBuinJdX6Gu1ohOgka4Ays8vZehQdcLZmDHRm1/T/676elXk8IMfFIQ1l2KEoR1YLQGtzkKG005LJyUlPtLDCSl6B67HY50duE3C0C3CIwktublZPPecss63bLHGgUl33TWCoqJqAMaOVfMfrd6a0+nwGR9nntkjrLkUIwztwGrhDL0wRXt+AZTV2rOnOstaC3OkrVad34l2j8HpdPCPf+QC8OWXxyyxX2P//mqOH6+nR48kHA7L9NwMCS6X29d22+VyhzVcZoShHVhNGJryC9FtsYKyWr/4QtWsb95sDas1VkJJAFdc0Y8uXeKpr5fMmRP5Hfb+c68r86IR/Tl/4AHVyHPYsPSw5lKMMJyCvLxCdu6sIDU1noMHazh6tM4CFmvseAxOp4Pbb1dn4C5ZUhRxq/Xo0ToOHqzxHaYS7bhcburr1R6GF17YHfEkr/7s6zBStKIbec6dqxp5FhVVh7WRpxGGU5Cbm8XVV6+mX78UAP71rz0Rt1hjpSJJM3t2f0Dtfo503bpemEaNiq4zMFpCW62XXaaOWZ09u3/EK4A2boyNMJ5u5NmnTwpZWUkcPRreRp5GGE6Brg7Yt08lvO69d2NELdba2qaKpGg8oKQl3O6m/SORrltvyi9Et8UKTVbrRRf1AaCqqjHiFUCxkvjXCCEYO1aJ4IYNx8P2vkYY2oHT6eCss9Th6KNHZ0TUYt22rYLGRsnQodFfkQTKap07dw0DB6YC8NBDYyNqtcaSt6atVh222bDheEQrgDweGVPzr9Hzr72lcGCEoR24XG4KCpSVtG7dsYgsSrqrpH9FTKRzHeFAW63TpvUAIDk5PqJWaywlnjUq0atCeXV1keuZtHdvFRUVDfTqlUzPntFdkeSPPuJ248bwFb8YYTgFOs76zDOTAEhJiY+Ixaq7Sr7zzkEAunSJj3iuIxxoq3X8+O5A5K3WWAtlAKSlJTBkSBoNDdJ3zngkiMW5B5p5bOHCCMMp0Bbr7NkDyMxMpLy8gSeeOCPsFqvOdSxaVATAsmUHI15THk601aTbLYebvLxC3nxzP4cO1dClSzyDBnWJCY9N02S1hm9xOpFY2T9yIvrfu3lzma/de6gJSBiEEFlCiA+EENu9PzNbua7R7/S2pX6P5wgh1nhfv8h7DKil0BarEMJntXbtmhgRi9X/1LY5cwbEjChA08IUTqvJn9zcLObNU6e2jR7dlY8+is5T21oj0vMPTaGUaC9VPZHMzCT690+lurqRXbvC0+U2UI/hbmC5lHIYsNx7vyWq/U5vu8zv8UeBx7yvPwrcGOB4Qsr48ZG1Wt955yClpfXExalzniNdUx5OBg3qQkZGAsXFtbjdNWF/f6fTwQ03DAagrs4T8f0U4SaSwhCr+TV/wh1OClQYLgde8P7+AnBFe18o1LbFGcCrnXl9JGgShvB/OVwuN9deuwZQX9Jo7SrZGnFxIiKxVn9qa1Xi9auvjkd8P0W4ieTc6/ya9hhKS2tjylsDfCWr4QrlBSoMvaSUBwG8P1v7pqQIIQqEEKuFEHrx7wEck1I2eO8XAf0CHE9I0aGkSAhDfn4p3//+YAAmTOgetV0l2yLS4YyPPioBYO7cgRHfTxFuhg1LJzk5jj17qsJ+/rbT6eCxxyZSV+chPT0+7J1GrUC4czynFAYhxIdCiI0t3C7vwPsMlFJOBq4BHhdCDAVa2jbaamZFCHGzV1wKSkpKOvDWwUOX7W3ZUhb2sr3580dSU6PaTk+YoAQqWrtKtob22CIhDB9+WOyroX/ssYkx57ElJMT52s9HIgGt9+xUVDTGnLcGFgwlSSlnSinHtnB7EygWQvQB8P5s8VsipTzg/bkTWAmcDhwGugshEryX9QcOtDGOp6WUk6WUk7OzszvwTwweaWkJDB2aTkODpLAw/A311q9XuY2JE7uH/b2tQFNlUvgXpvffPwRA//6p9OyZHNMeWzjr6TWvv66q8c46q0fMeWt5eYW43bXExakNrrW1jSHPsQQaSloKzPP+Pg9488QLhBCZQohk7+89gbOAzVJKCbiAK9t6vdWIVJ7B45E+a2HChNiqytDohWnTpvCV7Wl0PNtflGPNY4tUKM/lcrNkiRKG228fHnPeWm5uFtddt4a+fVNobJS8+GLo+7UFKgyPAOcJIbYD53nvI4SYLIR41nvNKKBACPElSggekVJu9j73v8DPhRA7UDmH5wIcT8iJlDB8/XUFlZWN9OuXSo8esbPr0x//sr2dOyvC+t6x7q3l5RXi8Sgx1sIQrsqg/PxSMjJUYGHixNjLr+l/r9utzoD++c+/DHmOJSBhkFIekVKeK6Uc5v1Z6n28QEp5k/f3z6SU46SUE7w/n/N7/U4p5RQp5WlSytlSytrW3ssqNCWgw1uyGusLE6jFqW9f1eVWC3O4FqdYn//c3Czy8rYCShhWrCgOW2XQvHmDKS2tJyND7cCG2PPWnE4H06apuT7nnOyQ51jMzucOEimP4csvYzuMBGpx2rBBxbc3bDge1kN7Yl0YnE4HS5ZMQwgoLa1j9uzVYasMWreuae6jvdV5a7hcbjZvLuP++0exatWRkIfRjDB0gLy8QnbvriQtTR3ac/hwbdgtVl2RFIs4nQ5+9jN1aM+iRfvCtsmsuLiGgwdryMhIICcn+g/naY0ZM3oxaFAXAGbOdIStMkh/9k8/PTY/+9oAWrx4Og8+ODYsORYjDB1AH9ozcKD6cvzzn7vDZrF++WVsW6yaq68eCIT30B499xMmxK7FCmqBKi5W0d633joYtuTvunVHgdj97Ot+bfqzHo4cixGGDqD/Q3S/kgce2BQWi/XIkVqKiqrp0iWeoUPTQ/peVqe4uNr3+5NPhqdsMdbDSNBktd5553AAxo3rGrbKIB1KOv30FluxRT26X5s/oc6xGGHoIE5nkws9ZEhamCxWlV8YN64b8fGxbbHOmbPG121y/vwRYVmcjDA0Wa169/3u3VVhOYO4vLyeHTsqSEwUMXHGuVUwwtBBXC43q1YdAVQb3FAuSrp5mH8YKdaah/mjF6eZM3sBUF8vw1K2aIShyWrNyUkjMzOR4uJahg/PCHll0FdfHUdKdQZDUpJZrsKFmekOoN3pRYumkZQUR2MjzJ4dOotVNw977z2161YfEhRLzcP80YvT5MkqpFBQUBoyl1qLclVVA1u3lhMfLzh8uDZmRVkjhGDSJDX/n39+NOTv1xRGil1RjgRGGDqAtljPP7+3z3q8556RIbNYdU5j+XIlPC+8sDvmmoe1hBbG/PzQLUxalP/xj914PDBgQCpz566JWVH2RwtDQUHohSHWK5IihRGGDuCfBJoyRS0QdXWekLrTU6Zk+do//OhHQ2JeFEB1+szISGD//moOHqw+9Qs6gRbl//3fDQAcPFhjRNnL5Mnqsx9Kj0F7bP4VSbEcRg03Rhg6SW6usppCabUCPPPMTgB6907mued2xUx/mLaIiwtPOMPpdDBsmKoC+9a3Qr/b1C74ewyq5Vnw0R6b3kh6/Hh9TIdRw40Rhk7SFM4IXeLT5XJz330bAfj2t/vHXPOwtghHOMnlcvsWpvz8UjPvXgYP7kJWVhIlJaqMOhQ4nQ4efngcDQ2SzMxEbrgh33hsYcQIQycZMSKDjIwEiopCF87Izy/15TKmTcuKueZhbeGfgA4FLpeb2bNX0dgoSUwULFw4zYiyF/8EdCjzDDqEevRofUyewRBJjDB0krg44VucQmW13nXXCL7+Wm2mmzatBxB7zcNao0kYQhPOyM8vZf78EQCccUYmF1zQ24iyH+EI5b322n4ALrywd8ydwRBpjDAEQKjDSfv2VXPoUA2ZmYm+WLdBkZOTRlZWEm53Lfv2Bd9jmz9/JNXV6sS86dONKPuTl1dISopaOrTHFuzEsMvlZvnyYgD+7//C0x/I0IQRhgDQlUlr14ZGGFavVhvppk3rgRCxu+O5JX77262+FsyhWpz0RkYtDAZFbm4Wf/rTDkB5DKFowb1ihRuPB9LS4hk3rpsJo4YZIwwBoL8IoQpn+AuDoTm5uVm+M5gLCo4GvQW3xyNZvVotQkYYmuPfgvvw4dC04D7jDBWqmjq1BwkJcb73NR5beAhIGIQQWUKID4QQ270/T+pyJYRwCiHW+91qhBBXeJ/7hxBil99zEwMZT7h55ZW9dO+eSGlpHTt3qlxAMK1WvTDpAzoMTTidDu6+Wy0SCxfuDXoL7sLCco4fr6d//1QGDOgSlL8ZTcyY0cvX0PGb3+wZ9MTwZ58dBtQZz4bwE6jHcDewXEo5DFjuvd8MKaVLSjlRSjkRmAFUAf/xu+Qu/byUcn2A4wkrU6ZkUVWl4tBr15YG1WqtrW3kiy+O+t7HcDI33pgDwK5dVUHf/GfCSG3jcrl91Xjvv38o6LH/zz5T83/mmT2D+ncN7SNQYbgceMH7+wvAFae4/krgXSllVYDvawmcTgfXXKPOB//qVpoAABLHSURBVHj88W1BtVq//PI4tbUeRo3KoHv3pID/XjSydWs5cd5P8BNPBLdqxQhD62gD6Le/HQ9AVlZSUBPDNTWNFBQcRQjjLUeKQIWhl5TyIID356lWxKuBV054bIEQ4ishxGNCiFZPuRdC3CyEKBBCFJSUlAQ26iAyb94gANauPRqUWmvdCsA/v2BaAZyMXpxmzeoNwOzZ/YO6OBlhaB3dM+yHPxxCRkYCBw7U8MQTZwQtMfzFF0epq/MwZkxXYxRFiFMKgxDiQyHExhZul3fkjYQQfYBxwPt+D98DjARygSzgf1t7vZTyaSnlZCnl5Ozs7I68dUiprfX4fg/GwTG6FcCbb6oa7szMJNMKoAX04nTttUqY9+6tCrhqRYvysWN1bN5cRlJSHMeP1xtRPgHdMywhIY5vfEOFehoaZNASw59+asJIkeaUwiClnCmlHNvC7U2g2Lvg64W/rVXxKuB1KWW9398+KBW1wPPAlMD+OeHF5XIzd+4azjhD7U7+yU+GBGy16rK8lSuVV/T887tMK4AW0IuTnpdPPjnMWWf1DGhx0qL81FOqP9Vpp6Wbjqqn4OyzlZH20UeBe/FamP0Tz8ZbjgyBhpKWAvO8v88D3mzj2jmcEEbyExWByk9sDHA8YUVbrbNnDwDg0KHaoNRaDxjQBY/XEbnlltOMKLRB794pjBqVQWVlY8DzrkX5wQc3A7BrV6UR5VOghWHlysBDeFqYtVEUFyeMtxwhAhWGR4DzhBDbgfO89xFCTBZCPKsvEkIMBgYAH53w+peEEBuADUBP4KEAxxNWtNV67rlq4Vixwh2UWus//Wk7AKNHd+Wpp0wrgFOhF+5gzJPT6SArS8W1L7mkjxGFUzBpUiZpafFs21YRcM8wp9PBY49N5Nixerp0iednP1tvhDlCBCQMUsojUspzpZTDvD9LvY8XSClv8rtut5Syn5TSc8LrZ0gpx3lDU3OllBWBjCdSnHFGJt27J7JzZyW7d1cG9LdcLrcvlHHXXSNMK4B20CQMgYcz3nhjP0VF1cTFKaE38942iYlxnHWWygUEI5yky7+rqhpN47wIYnY+B4H4eME55yiXWp+21lk+++wIuvvFBRf0Mq0A2oGe+88+O0xtbWOn/47L5eb669cCenevEeVTkZdXyIABqUCTMASSF3j++V2A8tZM47zIYYQhSJx7rjqgXjf+6ixTp2ZRW+thwoRu9OmjvnCmFUDb/P3vu8jJSaOmxuPbLd6ZxSk/v5SpU1U8+6KL+hhRbge5uVm+LqgffVQS0CbPd9896Pv/e/rpScZbjiBGGIKEDiGtWOH29U3qzOL03nuHANVq2NA+cnOzfPFtl8vd6cXpjjtG+M4YvugiNf9GlNvG6XSwaNE0ALZsKefKKzu/yXPx4n2Aaqnep0+qEeYIYoQhSFx0UW/i4qC4uJZNm8o6vTgZYeg4TqeDe+8dBahQRGd3oBcUlHL4cB05OWmMGJERiqFGJeef39vX6fass3p0Oi+QlBQPwKWX9vU9ZoQ5MhhhCBIzZvRixgz1hbjjji87tTjt21fFpk1lpKcnmM09HeTWW08DYO/eaubNG9ypxemdd5QoX3RRb9PmvAO4XG7c7lpAGTadCf1IKXnrrQMAXHppn6COz9BxjDAEkblz1S7c//ynuEMVFXpjz/vvq4Xp3HMdfPrpYbOxpwOsX3+MxES1mD/11M5OLU5vv30QgIsvNgtTe9Ge8YsvTiE+XtDYKJk9u+N5gS++OMaBAzX065fqO87WEDmMMASRrKxE3+8daeqmN/a8+OIeAAYPTjMbezqAXpzuuGM4oA6rb2/SUovyoUM1fP75UVJS4hBCGFFuJ3qT5xVX9GPmTAceD8ybN7jDeYFly5S3cMklfYy3ZgGMMAQJl8vND35QwDnnqBDQt7/dt92Lk9Pp4MUXp/Lxx6oVwIsv7jYbezqAXpzuvXcUqanxbNxYxp/+dHq7Fictyo89tg2ACRO6cd11pg1Ge9GbPAGuvlp1Gl6//li78wJamN96S3lrl17a17TBsABGGIKEXpzuukt9IT7++DCLFk1rt+VUXFyDPgTupz81bTA6gl6cMjISuewylbjcs6eqXYuTrnx5/HG123zTpnIjyp3kiiv6kpQUx8qVboqLa9r1mtzcLK68chWff36U1NR44uNNGwwrYIQhSOjF6fzze9GrVzLbtlXQpUtCuy2nhx/eAqjEm9nY03n0+RgvvbSn3a8ZOLALdXVqU/4Pf5hjRKGTPP30TiZN6o7HA6++WgScumTb6XTwve+pXmMDBqRy3XVrjDBbACMMQSYhIc7XCvqf/2zf4vTXv+5g69YK0tPjWbhwmtnYEwAbNx4nIyOBjRvL+OortSfhVIvT/PlfASqM9OKLe8y8d5Lc3Cw2blTncC9atK9dJdt1dR5ffmHbtgrTBsMiGGEIAR6PigktXLjX16KhrcXpued2A/CjHw2lS5cEs7EnAKZP70F9vbL+X3557ykXp2XLDvDvf6uduy+8MMWIcgA4nQ5eeWUqoEKp7dns9soreykqqiY+XnDffaOMt2wRjDCEgMsu60t8vODo0XqWLTvY4uKkk25HjtSyadNxACZO7O4TD7Oxp3M4nQ4eeWQcoA5OOtV+Et2w0OnMZsKE7kaUA+Tii/syapTaHDhkSFqbouDxSH75y00A3HnncH7zm7FGmC2CEYYQ4HQ6+NGPhgBw++3rWlycdDXML36xkZoaD7m5mfzsZ+tN0i0I3HbbMHr2TKK8vIExY7qdtDhpUW5o8LBxoxLlGTMcRpSDgC79FQIKCo7y7LM7T7pGz//bbx9kz54qevZM8s2/EWZrYIQhRCxYMJb09Hj2769h/PjuJy1OukT1mWfUF2frVlMNEyw++qiE+nrp+/2FF3Y3e16L8oIFW9izp4q+fVP44x+3G1EOEO0Zv/bamdx2m9qJfsstX7BiRfPGknr+7713AwCzZw/g2mubSoSNMEeegIRBCDFbCLFJCOERQkxu47oLhRBbhRA7hBB3+z2eI4RYI4TYLoRYJISImpO/1607Rlyc2qizYoW7meXkcrl55JFCnnlmp++ktttuG2ZEIQjoxen1189k8uRMAH74wwLf4uRyucnPL+WJJ87wndR2/Hi9EeUgoEu29WFHXbsmUF8v+fvfdwNNeTan08Gdd45g48YykpPjWLx4n5l/ixGox7AR+A7w39YuEELEA08As4DRwBwhxGjv048Cj0kphwFHgRsDHI8l0IvTG2+cxSWXqPYKP/rR59x0Uz5/+MNWrrpqFZ9/XupLek6dmmVOagsS/ovTL34xCiGgvl5y++3rfXOfk5PGffdt9BNls28kGPhvdvvWt7J98/v22wd56KHNvjzbkiX7uP9+dYpvba2HW24xlUhWQ+gW0QH9ESFWAndKKQtaeG468Csp5QXe+/d4n3oEKAF6SykbTryuLSZPniwLCk56K8uQl1dIbm4WTqeDiooGhg9/l4MHa0hLi6Oy0sM3vtGDTz45AkBaWjzLln0DoNNdQQ2tc++9G3j44UKEACnVJqwtW8rZurUcgKuu6s+KFW4z7yHgww+LufDCj2lsVGvM9OlZXHBBb371K+WpJSXFcdddI3jqqa/N/IcJIcTnUspWozuacOQY+gH7/O4XeR/rARyTUjac8Ljt8bec0tMTWLJkOvHxUFmpTCgtCqmpcSxb9g2cTodJuoWIBQvGMnFiN9+u8jfeOOAThQcfHMOiRdNNJUyImDmzl68IA2DVqlKfKKSkxPHuu9/goYdMJZIVOaUwCCE+FEJsbOF2eTvfo6WOWLKNx1sbx81CiAIhREFJSeBny4aTujoP3bolceWV/ejSJZ6cHNW7/s47RzSzkkzSLfisXFlCUVE1P/vZMNLTE3zHUF55ZX/uv19FNI0ohwaXy83ixfu4//5RZGYmMmxYOgBjxnTlnXe+yYwZ6tRDM/8WREoZ8A1YCUxu5bnpwPt+9+/x3gRwGEho6bq2bpMmTZJ2YcWKYtmz5xtyxYpiKaWUv/99oRRisbzuutXNHjcEHzP3kcPMvTUBCmQ71thwhJLygWHeCqQk4GpgqXeQLuBK73XzgDfDMJ6w4p8MdbncPPxwIb/73XjGju1mXOgQY+Y+cpi5tzcBJZ+FEN8G/gxkA8eA9VLKC4QQfYFnpZQXea+7CHgciAf+LqVc4H18CLAQyALWAXOllLWnel+rJ59bwz8prdHlkyaEFFrM3EcOM/fWob3J56BUJYUbuwqDwWAwRBIrVSUZDAaDwUYYYTAYDAZDM4wwGAwGg6EZRhgMBoPB0AwjDAaDwWBohi2rkoQQJUD7D/VtTk/UxjqrYcbVMcy4OoYZV8eI1nENklJmn+oiWwpDIAghCtpTrhVuzLg6hhlXxzDj6hixPi4TSjIYDAZDM4wwGAwGg6EZsSgMT0d6AK1gxtUxzLg6hhlXx4jpccVcjsFgMBgMbROLHoPBYDAY2iAqhUEIMVsIsUkI4RFCTD7huXuEEDuEEFuFEC0eI+ptEb5GCLFdCLHI2y482GNcJIRY773tFkKsb+W63UKIDd7rQt45UAjxKyHEfr+xXdTKdRd653CHEOLuMIzrt/+/vbMLsaqK4vjvj6ZRiY6JOaaQgkj2lIXYJ5JiKqEVEdNLkkFYCPmWIEjkk0E9BJVQSR9I3xmDKDqV0JMmDc5ojObVggankb60CKxg9bD35LnHc2ZOc+85F3X94HD33Xudc9ZZe81eZ3/c2ZKOSuqVtEPSpBy5Suw10vNLGh/ruBZ96YaydEncc6akfZL6ov8/nSGzSNKZRP1uKluveN9h60WBl6K9eiXNr0CnuQk7HJJ0VtL6lEwl9pK0TdJpSUcSeZMldcV2qEtSW865q6PMcUmrm6JQkU0bLrYDuBGYS2oDIWAe0AOMB2YBJ4AxGed/AHTE9FbgyZL1fQHYlFP2PTClQts9S9i/eziZMdF2s4Fx0abzStZrKec3ddoCbGmVvYo8P/AUsDWmO4D3K6i7dmB+TE8Avs3QaxGwsyp/KlovwApgN2EDr4XAgYr1GwP8SFjnX7m9gLuB+cCRRN7zwIaY3pDl84QtC07Gz7aYbmtUn0uyx2BmfWZ2LKNoFfCemZ0zs++AGrAgKSBJwD3ARzHrLeD+snSN93sYeLese5TAAqBmZifN7C/CnhpFt3odFWa2187vD74fmFHm/UagyPOvIvgOBF9aHOu6NMxswMy6Y/p3oI+LZx/1VcDbFtgPTJLUXuH9FwMnzGy0P5xtCDP7EkjvbZr0obx26F6gy8x+MbNfgS5gWaP6XJKBYRiuB35IfO/nwj+ca4HfEo1QlkwzuQsYNLPjOeUG7JX0taQnStQjybrYnd+W030tYscyWUN4u8yiCnsVef7/ZKIvnSH4ViXEoaubgQMZxbdJ6pG0W9JNFak0Ur202qc6yH85a4W9AK4zswEIQR+YmiFTit3GNnqBViHpM2BaRtFGM8vbIjTrjS29LKuITCEK6vgIw/cW7jCzU5KmAl2Sjsa3i1EznF7Aq8BmwjNvJgxzrUlfIuPchpe3FbGXpI3AP8D2nMs03V5ZqmbkleZH/xdJ1wAfA+vN7GyquJswXPJHnD/6FJhTgVoj1Usr7TUOWEnYiz5Nq+xVlFLsdtEGBjNbMorT+oGZie8zgFMpmZ8I3dix8U0vS6YpOkoaCzwI3DLMNU7Fz9OSdhCGMRpq6IraTtJrwM6MoiJ2bLpecWLtPmCxxQHWjGs03V4ZFHn+IZn+WM8TuXCooOlIuoIQFLab2Sfp8mSgMLNdkl6RNMXMSv2/QAXqpRSfKshyoNvMBtMFrbJXZFBSu5kNxGG1rE2y+wnzIEPMIMytNsTlNpTUCXTEFSOzCJH/q6RAbHD2AQ/FrNVAXg+kUZYAR82sP6tQ0tWSJgylCROwR7Jkm0VqXPeBnPsdBOYorN4aR+iGd5as1zLgGWClmf2ZI1OVvYo8fyfBdyD40hd5waxZxDmMN4A+M3sxR2ba0FyHpAWENuDnkvUqUi+dwKNxddJC4MzQMEoF5PbaW2GvBEkfymuH9gBLJbXFYd+lMa8xyp5tb8VBaND6gXPAILAnUbaRsKLkGLA8kb8LmB7TswkBowZ8CIwvSc83gbWpvOnAroQePfH4hjCkUrbt3gEOA73RMdvTesXvKwirXk5UpFeNMJZ6KB5b03pVaa+s5weeIwQugCuj79SiL82uwEZ3EoYRehN2WgGsHfIzYF20TQ9hEv/2CvTKrJeUXgJejvY8TGI1Ycm6XUVo6Ccm8iq3FyEwDQB/x7brccKc1OfA8fg5OcreCryeOHdN9LMa8Fgz9PFfPjuO4zh1XG5DSY7jOM4IeGBwHMdx6vDA4DiO49ThgcFxHMepwwOD4ziOU4cHBsdxHKcODwyO4zhOHR4YHMdxnDr+BVmXbqMihBeGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Generate a sequence of numbers from -10 to 10 with 100 steps in between\n",
    "x = np.linspace(-10, 10, 100)\n",
    "# Create a second array using sine\n",
    "y = np.sin(x)\n",
    "# The plot function makes a line chart of one array against another\n",
    "plt.plot(x, y, marker=\"x\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "uuid": "ad1b06f7-e03a-4938-9d59-5bb40e848553"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>John</td>\n",
       "      <td>New York</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>Paris</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Peter</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Linda</td>\n",
       "      <td>London</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name  Location  Age\n",
       "0   John  New York   24\n",
       "1   Anna     Paris   13\n",
       "2  Peter    Berlin   53\n",
       "3  Linda    London   33"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# create a simple dataset of people\n",
    "data = {'Name': [\"John\", \"Anna\", \"Peter\", \"Linda\"],\n",
    "        'Location' : [\"New York\", \"Paris\", \"Berlin\", \"London\"],\n",
    "        'Age' : [24, 13, 53, 33]\n",
    "       }\n",
    "\n",
    "data_pandas = pd.DataFrame(data)\n",
    "# IPython.display allows \"pretty printing\" of dataframes\n",
    "# in the Jupyter notebook\n",
    "display(data_pandas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Peter</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Linda</td>\n",
       "      <td>London</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name Location  Age\n",
       "2  Peter   Berlin   53\n",
       "3  Linda   London   33"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select all rows that have an age column greater than 30\n",
    "display(data_pandas[data_pandas.Age > 30])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### mglearn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Python 2 versus Python 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Versions Used in this Book"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python version: 3.7.0 (default, Jun 28 2018, 13:15:42) \n",
      "[GCC 7.2.0]\n",
      "pandas version: 0.23.4\n",
      "matplotlib version: 3.0.0\n",
      "NumPy version: 1.15.2\n",
      "SciPy version: 1.1.0\n",
      "IPython version: 6.4.0\n",
      "scikit-learn version: 0.21.dev0\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "print(\"Python version:\", sys.version)\n",
    "\n",
    "import pandas as pd\n",
    "print(\"pandas version:\", pd.__version__)\n",
    "\n",
    "import matplotlib\n",
    "print(\"matplotlib version:\", matplotlib.__version__)\n",
    "\n",
    "import numpy as np\n",
    "print(\"NumPy version:\", np.__version__)\n",
    "\n",
    "import scipy as sp\n",
    "print(\"SciPy version:\", sp.__version__)\n",
    "\n",
    "import IPython\n",
    "print(\"IPython version:\", IPython.__version__)\n",
    "\n",
    "import sklearn\n",
    "print(\"scikit-learn version:\", sklearn.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A First Application: Classifying Iris Species\n",
    "![sepal_petal](images/iris_petal_sepal.png)\n",
    "#### Meet the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "iris_dataset = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keys of iris_dataset:\n",
      " dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names', 'filename'])\n"
     ]
    }
   ],
   "source": [
    "print(\"Keys of iris_dataset:\\n\", iris_dataset.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _iris_dataset:\n",
      "\n",
      "Iris plants dataset\n",
      "--------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 150 (50 in each of three classes)\n",
      "    :Number of Attributes: 4 numeric, pre\n",
      "...\n"
     ]
    }
   ],
   "source": [
    "print(iris_dataset['DESCR'][:193] + \"\\n...\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target names: ['setosa' 'versicolor' 'virginica']\n"
     ]
    }
   ],
   "source": [
    "print(\"Target names:\", iris_dataset['target_names'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature names:\n",
      " ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n"
     ]
    }
   ],
   "source": [
    "print(\"Feature names:\\n\", iris_dataset['feature_names'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type of data: <class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print(\"Type of data:\", type(iris_dataset['data']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of data: (150, 4)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of data:\", iris_dataset['data'].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First five rows of data:\n",
      " [[5.1 3.5 1.4 0.2]\n",
      " [4.9 3.  1.4 0.2]\n",
      " [4.7 3.2 1.3 0.2]\n",
      " [4.6 3.1 1.5 0.2]\n",
      " [5.  3.6 1.4 0.2]]\n"
     ]
    }
   ],
   "source": [
    "print(\"First five rows of data:\\n\", iris_dataset['data'][:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type of target: <class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print(\"Type of target:\", type(iris_dataset['target']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of target: (150,)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of target:\", iris_dataset['target'].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target:\n",
      " [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2]\n"
     ]
    }
   ],
   "source": [
    "print(\"Target:\\n\", iris_dataset['target'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Measuring Success: Training and Testing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    iris_dataset['data'], iris_dataset['target'], random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape: (112, 4)\n",
      "y_train shape: (112,)\n"
     ]
    }
   ],
   "source": [
    "print(\"X_train shape:\", X_train.shape)\n",
    "print(\"y_train shape:\", y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_test shape: (38, 4)\n",
      "y_test shape: (38,)\n"
     ]
    }
   ],
   "source": [
    "print(\"X_test shape:\", X_test.shape)\n",
    "print(\"y_test shape:\", y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### First Things First: Look at Your Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f1e5c9a3ef0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1e5c9520f0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1e5c9794e0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1e5c91f978>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f1e5c8c7e48>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1e5c8f8320>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1e5c8a27f0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1e5c84acf8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f1e5c84ad30>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1e5c822710>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1e5c7cec18>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1e5c7fe160>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f1e5c7a5668>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1e5c74db70>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1e5c77f0b8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1e5c7265c0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create dataframe from data in X_train\n",
    "# label the columns using the strings in iris_dataset.feature_names\n",
    "iris_dataframe = pd.DataFrame(X_train, columns=iris_dataset.feature_names)\n",
    "# create a scatter matrix from the dataframe, color by y_train\n",
    "pd.plotting.scatter_matrix(iris_dataframe, c=y_train, figsize=(15, 15),\n",
    "                           marker='o', hist_kwds={'bins': 20}, s=60,\n",
    "                           alpha=.8, cmap=mglearn.cm3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Building Your First Model: k-Nearest Neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=None, n_neighbors=1, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Making Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_new.shape: (1, 4)\n"
     ]
    }
   ],
   "source": [
    "X_new = np.array([[5, 2.9, 1, 0.2]])\n",
    "print(\"X_new.shape:\", X_new.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction: [0]\n",
      "Predicted target name: ['setosa']\n"
     ]
    }
   ],
   "source": [
    "prediction = knn.predict(X_new)\n",
    "print(\"Prediction:\", prediction)\n",
    "print(\"Predicted target name:\",\n",
    "       iris_dataset['target_names'][prediction])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Evaluating the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set predictions:\n",
      " [2 1 0 2 0 2 0 1 1 1 2 1 1 1 1 0 1 1 0 0 2 1 0 0 2 0 0 1 1 0 2 1 0 2 2 1 0\n",
      " 2]\n"
     ]
    }
   ],
   "source": [
    "y_pred = knn.predict(X_test)\n",
    "print(\"Test set predictions:\\n\", y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set score: 0.97\n"
     ]
    }
   ],
   "source": [
    "print(\"Test set score: {:.2f}\".format(np.mean(y_pred == y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set score: 0.97\n"
     ]
    }
   ],
   "source": [
    "print(\"Test set score: {:.2f}\".format(knn.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary and Outlook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set score: 0.97\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    iris_dataset['data'], iris_dataset['target'], random_state=0)\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=1)\n",
    "knn.fit(X_train, y_train)\n",
    "\n",
    "print(\"Test set score: {:.2f}\".format(knn.score(X_test, y_test)))"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:py37]",
   "language": "python",
   "name": "conda-env-py37-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
